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Drones
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The Applications of Drones in Logistics 2nd Edition
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The Applications of Drones in Logistics 2nd Edition

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Innovative Urban Mobility".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 3249

Special Issue Editor

Dr. Tamás Bányai

E-Mail Website
Guest Editor
Institute of Logistics, University of Miskolc, 3515 Miskolc-Egyetemváros, Hungary
Interests: transportation; supply chain; city logistics; optimization; logistics; Industry 4.0; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Fourth Industrial Revolution led to significant changes in logistics. Industry 4.0 technologies make it possible to transform conventional supply chain solutions into cyber–physical systems. The application of drones can be categorized into two different fields of logistics: the first is in-plant material supply, which includes both warehouse operations and the material supply of production and assembly cells; the second is the application of cargo drones for both light and heavy payloads.

We invite researchers in the global logistics and supply chain management community to contribute original research papers, as well as review articles and empirical studies, which will stimulate debate within this topic.

Potential topics include, but are not limited to, the following:

  • Design and optimization of drone-based in-plant supply solutions;
  • Application of drones in warehouse logistics;
  • Big data in drone-based logistics;
  • Energy efficiency and sustainability of drone-based logistics solutions;
  • Legal aspects of drones in logistics;
  • Security and surveillance in drone-based logistics;
  • Standardization potentials in drone logistics.

Dr. Tamás Bányai
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • drone-based warehouse logistics
  • intralogistics with drones
  • optimization of drone-based logistics solutions
  • sustainability of drone systems
  • security and surveillance in drone-based logistics

Related Special Issue

Published Papers (2 papers)

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Research

21 pages, 12565 KiB  
Article
An Image Processing Approach for Real-Time Safety Assessment of Autonomous Drone Delivery
by Assem Alsawy, Dan Moss, Alan Hicks and Susan McKeever
Drones 2024, 8(1), 21; https://doi.org/10.3390/drones8010021 - 15 Jan 2024
Viewed by 1643
Abstract
The aim of producing self-driving drones has driven many researchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic [...] Read more.
The aim of producing self-driving drones has driven many researchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic way to verify the safety of the delivery stage. One of the primary steps in the delivery operation is to ensure that the dropping zone is a safe area on arrival and during the dropping process. This paper proposes an image-processing-based classification approach for the delivery drone dropping process at a predefined destination. It employs live streaming via a single onboard camera and Global Positioning System (GPS) information. A two-stage processing procedure is proposed based on image segmentation and classification. Relevant parameters such as camera parameters, light parameters, dropping zone dimensions, and drone height from the ground are taken into account in the classification. The experimental results indicate that the proposed approach provides a fast method with reliable accuracy based on low-order calculations. Full article
(This article belongs to the Special Issue The Applications of Drones in Logistics 2nd Edition)
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34 pages, 5990 KiB  
Article
Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism
by Shengqi Kang and Xiuwen Fu
Drones 2024, 8(1), 14; https://doi.org/10.3390/drones8010014 - 07 Jan 2024
Viewed by 1187
Abstract
The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable [...] Read more.
The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable solution to this challenge. To this end, we propose a Blockchain-enabled Infection Sample Collection system (BISC) consisting of a two-echelon drone-assisted mechanism. The system utilizes collector drones to gather samples from user points and transport them to designated transit points, while deliverer drones convey the packaged samples from transit points to testing centers. We formulate the described problem as a Two-Echelon Heterogeneous Drone Routing Problem with Transit point Synchronization (2E-HDRP-TS). To obtain near-optimal solutions to 2E-HDRP-TS, we introduce a multi-objective Adaptive Large Neighborhood Search algorithm for Drone Routing (ALNS-RD). The algorithm’s multi-objective functions are designed to minimize the total collection time of infection samples and the exposure index. In addition to traditional search operators, ALNS-RD incorporates two new search operators based on flight distance and exposure index to enhance solution efficiency and safety. Through a comparison with benchmark algorithms such as NSGA-II and MOLNS, the effectiveness and efficiency of the proposed ALNS-RD algorithm are validated, demonstrating its superior performance across all five instances with diverse complexity levels. Full article
(This article belongs to the Special Issue The Applications of Drones in Logistics 2nd Edition)
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